<?xml version="1.0" encoding="UTF-8"?>
<XML><RECORDS>
<RECORD>
	<REFERENCE_TYPE>10</REFERENCE_TYPE>
	<AUTHORS>
		<AUTHOR>Ventura, Joao</AUTHOR>
	</AUTHORS>
	<YEAR>1997</YEAR>
	<TITLE>Neural Networks implementation in parallel distributed processing systems</TITLE>
	<SECONDARY_TITLE>Graduate Degree</SECONDARY_TITLE>
	<PLACE_PUBLISHED>Lisbon, Portugal</PLACE_PUBLISHED>
	<PUBLISHER>Departamento de Inform&Atilde;&iexcl;tica da Faculdade de Ci&Atilde;&ordf;ncias e Tecnologia da Universidade Nova de Lisboa</PUBLISHER>
	<KEYWORDS>
		<KEYWORD>Neural Networks</KEYWORD>
		<KEYWORD>Distributed Systems</KEYWORD>
		<KEYWORD>PVM</KEYWORD>
	</KEYWORDS>
	<ABSTRACT>In this work I will try to present a distributed implementation of the back-propagation algorithm which performs better than a centralized version. As the basis for this work I used the Matrix Back-Propagation Algorithm developed by Davide Anguita. This algorithm is a highly efficient version of the standard back-propagation algorithm using the &acirc;€ślearning by epoch&acirc;€ť mode of training. Because it uses optimized matrix operations to perform the usual operations in the learning phases of the neural network, this method achieves a very good performance. Based on this work I have implemented three distributed versions, each exploring a different aspect of distribution.</ABSTRACT>
</RECORD>
</RECORDS></XML>